The regular telephone voice channel has been used for digital data transmission since the early 1960's. The digital data is modulated onto a sine wave carrier signal whose frequency is within the voice band for transmission. The digital data is demodulated off the carrier signal after passing through the telephone channel. The device used to modulate digital data onto a carrier or demodulate digital data from a carrier is known as a modem. Three basic modulation techniques used by such modems are Amplitude Modulation, Frequency Modulation or Frequency Shift Keying, and Phase Modulation or Phase Shift Keying.
A regular telephone voice channel has a pass band from about 300 Hz to about 3300 Hz . However, the telephone voice channel is characterized by amplitude and phase deterioration at both the low and high frequency ends of this frequency band. The deterioration introduced by the telephone channel may make it difficult to confidently make a decision at a receiver as to the original transmitting value of a received signal. Accordingly, it is desirable to compensate for the undesirable frequency response characteristics of the voice telephone channel, so that an approximately flat frequency response is achieved across the voice pass band.
With some primary channel compensation techniques, such as compromise equalization, a regular telephone voice channel can transmit digital data at 1200 bits per second in full duplex, meaning two-way transmission at the same time.
A compromise equalizer is a transversal digital filter whose frequency response is the inverse of that of the average telephone channel. The use of the compromise equalizer in a modem brings a telephone voice channel one step closer to the desired flat voice band frequency response. However, since the frequency response of a particular telephone channel can differ very much from the average and varies with time, the compensation provided by the compromise equalizer is very limited.
The demand for higher transmission rates and the advancement of signal processing technology have led to the application of adaptive channel equalization. Adaptive channel equalization typically involves use of a digital filter with adaptive filter coefficients--i.e. a digital filter whose coefficients vary in time. An adaptive channel equalizer sets up its filter coefficients to model the inverse frequency response of a particular voice telephone channel at the beginning of a transmission session according to set of predetermined training data and keeps track of any channel variation thereafter by adaptively changing its filter coefficients. The adaptive channel equalizer's filter coefficients are set up according to each different individual telephone channel and any necessary changes are made continuously along with the variations of the particular telephone channel. Thus, the compensation provided by the adaptive channel equalizer is quite good. The adaptive channel equalizer has become a major component of high speed modems whose transmission rate is 2400 bits per second or higher.
The most commonly used adaptive signal processing algorithm for setting up filter coefficients and for keeping track of channel variations is the adaptive Least Mean Square (LMS) algorithm. The application of the LMS algorithm to the channel equalization problem is disclosed in R. W. Lucky, "Automatic Equalization for Digital Communication," Bell System Tech.J., Vol. 44, pp. 547-588, April 1965. Based on the error between known training data and a received signal formed by transmitting the training data via a particular telephone channel, the LMS algorithm sets up filter coefficients according to an approximate gradient one small quantity at a time, so as to make the error as small as possible. The adaptive channel equalizer employing the LMS algorithm sufficiently reduces the error introduced into transmitted data by the telephone channel so that a confident decision can be made about the original transmitting value of a received signal. A significant advantage of using the LMS algorithm for channel equalization is that the LMS algorithm requires a relatively small amount of computation and can be easily implemented using a VLSI chip.
Thus, the use of an adaptive LMS channel equalizer can significantly increase usable channel capacity and make the high speed modem a reality. However, the initial convergence speed of an adaptive LMS channel is slow and the minimum Mean Square Error (MSE) is high for higher speed modems. With a conventional LMS adaptive channel equalizer, one has to trade a large MSE for a fast convergence time.
An important component of the MSE of a conventional LMS adaptive channel equalizer is the channel additive noise. The conventional LMS adaptive channel equalizer only identifies and tracks channel parameters but pays no attention to noise filtering.
Alternative adaptive signal processing algorithms for better channel equalization have been proposed: see e.g., J. M. Cioffi, "Fast Transversal Filter Applications for Communications Applications," Ph.D. Dissertation, Stanford University, 1984; B. Mulgrew and C. F. N. Cowan, "An Adaptive Kalman Equalizer: Structure and Performance," IEEE Tran. on Acoust., Speech. SIgnal Processing, Vol. ASSP-35, No. 12, pp. 1727-1735, December 1987; C. A. Belfior and J. H. Park, "Decision Feedback Equalization," Proc. IEEE, Vol. 67,(8), pp. 1143-1156, August 1979. However, these equalizers all require more computation power than the conventional LMS channel equalizer. The rapid convergence provided by the algorithms utilized in these equalizers is only required in the startup period when the filter coefficients are being set up. Once the filter coefficients characteristic of the inverse frequency response of a particular telephone channel are identified, the required speed to track the slowly time varying channel is much slower. Hence, the computation power of many fast algorithms is wasted in normal operation.
In view of the foregoing, it as an object of the present invention to provide an adaptive channel equalizer which overcomes the shortcomings of the conventional LMS channel equalizer, which is structurally simple, and which requires a minimum of computation power. More particularly, it is an object of the present invention to provide an adaptive channel equalizer which not only adaptively estimates the inverse frequency response of a particular telephone channel, but also smooths received data to reduce the effects of channel additive noise so as to achieve a smaller minimum mean square error or a faster convergence time.